19 citations · 23 across the 8 of their papers we have counts for
5 papers · 1 filter
From Show Programmes to Data: Designing a Workflow to Make Performing Arts Ephemera Accessible Through Language Models
Clarisse Bardiot, Pierre-Carl Langlais, Bernard Jacquemin +5
Many heritage institutions hold extensive collections of theatre programmes, which remain largely underused due to their complex layouts and lack of structured metadata. In this pa…
Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Pierre-Carl Langlais, Pavel Chizhov, Catherine Arnett +8
Large Language Models (LLMs) are pre-trained on large amounts of data from different sources and domains. Such datasets often contain trillions of tokens, including large portions…
Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family
Pierre-Carl Langlais, Pavel Chizhov, Mattia Nee +7
We introduce a new generation of small reasoning models for RAG, search, and source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a large synthetic dataset em…
Who Benchmarks the Benchmarks? Towards Comprehensive Evaluation of Commonsense Reasoning Benchmarks
Pavel Chizhov, Mattia Nee, Anton Changalidis +4
Commonsense reasoning is a key language model capability, as it is purportedly a prerequisite for many basic tasks, unlike specific factual knowledge. It is often measured with mul…
Towards Best Practices for Open Datasets for LLM Training
Stefan Baack, Stella Biderman, Kasia Odrozek +36
Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in…